# Set seed for reproducibility
np.random.seed(0)
# Create a normally distributed 500 row timeseries with values from 0 to 1
ts = pd.Series(np.random.randn(500), index=pd.date_range('1/1/2000', periods=500))
# Add another column with a uniformly distributed 500 row timeseries with values from 0 to 1
ts = pd.concat([ts, pd.Series(np.random.rand(500), index=pd.date_range('1/1/2000', periods=500))], axis=1)
# Add another column with a chi-squared distributed 500 row timeseries with values from 0 to 1
ts = pd.concat([ts, pd.Series(np.random.chisquare(2, 500), index=pd.date_range('1/1/2000', periods=500))], axis=1)
# Add another column with a student-t distributed 500 row timeseries with values from 0 to 1
ts = pd.concat([ts, pd.Series(np.random.standard_t(2, 500), index=pd.date_range('1/1/2000', periods=500))], axis=1)
# Rename the columns to 'Normal' and 'Uniform'
ts.columns = ['Normal', 'Uniform', 'Chi-squared', 'Student-t']